CN112954739A - Millimeter wave MEC unloading transmission method based on circular game algorithm - Google Patents
Millimeter wave MEC unloading transmission method based on circular game algorithm Download PDFInfo
- Publication number
- CN112954739A CN112954739A CN202110102950.5A CN202110102950A CN112954739A CN 112954739 A CN112954739 A CN 112954739A CN 202110102950 A CN202110102950 A CN 202110102950A CN 112954739 A CN112954739 A CN 112954739A
- Authority
- CN
- China
- Prior art keywords
- matching
- transmission
- fixed
- user
- utility function
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 16
- 206010042135 Stomatitis necrotising Diseases 0.000 claims abstract 2
- 125000004122 cyclic group Chemical group 0.000 claims abstract 2
- 201000008585 noma Diseases 0.000 claims abstract 2
- 230000007246 mechanism Effects 0.000 claims description 2
- 238000004891 communication Methods 0.000 abstract description 10
- 238000004364 calculation method Methods 0.000 abstract description 7
- 238000004088 simulation Methods 0.000 description 11
- 238000005516 engineering process Methods 0.000 description 8
- 238000011160 research Methods 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000000280 densification Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
本发明公开了一种基于循环博弈算法的毫米波MEC卸载传输方法，主要解决现有技术进行毫米波MEC计算卸载传输策略仅适用于单一用户场景，且卸载传输能量效率低，传输时延大的问题。其实现方案是：1)利用非合作式博弈论法设置卸载传输时用户匹配参数；2)计算所有匹配对的效用函数；3)利用效用函数判决匹配对的匹配成功与否；4)对匹配成功的匹配对进行重复匹配判断，保留效用函数最大的匹配对，剩余的匹配对被打破；5)检测匹配完成情况，若完成，则由匹配对得到的用户顺序进行非正交多址接入NOMA传输，完成MEC数据卸载，否则，返回3)。本发明大幅度减少了所需的能量效率和传输时延，可用于基于毫米波通信传输。
The invention discloses a millimeter wave MEC offload transmission method based on a cyclic game algorithm, which mainly solves the problem that the offload transmission strategy of the millimeter wave MEC calculation in the prior art is only suitable for a single user scenario, and the offload transmission energy efficiency is low and the transmission delay is large. question. The implementation scheme is: 1) use the non-cooperative game theory method to set the user matching parameters during offload transmission; 2) calculate the utility function of all matching pairs; 3) use the utility function to judge whether the matching of the matching pairs is successful or not; 4) pair matching Repeat the matching judgment for the successful matching pairs, keep the matching pairs with the largest utility function, and the remaining matching pairs are broken; 5) Detect the completion of the matching, if completed, perform non-orthogonal multiple access on the users obtained from the matching pairs in sequence NOMA transmission, complete MEC data unloading, otherwise, return to 3). The invention greatly reduces the required energy efficiency and transmission delay, and can be used for communication transmission based on millimeter waves.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a millimeter wave MEC unloading method which can be used for millimeter wave communication transmission.
Background
In the future 5G/B5G communication, "high computation traffic" will show explosive growth, such as virtual reality applications, ultra-clear video streaming, large-scale man-machine interaction games, AI computation processing, and the like, and the mobile terminal faces unprecedented overload computation challenges, which easily causes serious problems such as service delay or interruption, instantaneous power consumption surge, and the like. The mobile edge computing MEC is a distributed computing technology, adopts a distributed cloud architecture, directly unloads computing tasks to nearby infrastructure, namely a micro base station provided with an MEC server, reduces needed computing delay and local energy consumption of users, and can greatly reduce the load of a single computing server, thereby better solving the problem of computing unloading of a mobile terminal. Therefore, in a 5G/B5G mobile network, MEC technology can adapt to a variety of different business scenarios, including smart mobile terminals, VR virtual reality applications, holographic video or imagery, unmanned internet of vehicles.
The data transmission scheme of the existing MEC technology includes two types, one type of computation offload transmission is mainly based on decimetric wave frequency band communication and may be referred to as a "decimetric wave mect technology", and the other type of computation offload transmission is mainly based on millimeter wave frequency band communication and may be referred to as a "millimeter wave MEC technology". With the miniaturization and the densification of the 5G/B5G communication network, the quantity of high-computation-capacity services is greatly increased, the frequency spectrum resources of the traditional decimetric wave communication are limited, and the large data volume is required to be unloaded and transmitted while carrying intensive computation tasks, so that the computation tasks are overtime and even fail to work. Therefore, how to optimize the energy transmission efficiency and reduce the transmission delay is an important issue in the MEC transmission problem.
The millimeter wave MEC has millimeter wave communication with rich spectrum resources, can naturally serve the MEC technology, greatly reduces the time delay of a calculation task by realizing high-speed MEC unloading transmission, and further supports large-scale calculation task unloading. Compared with the visible millimeter wave MEC technology, the method has great advantages for the characteristics and the performance of different calculation unloading technologies according to the existing research. However, most of the related documents in the prior art are limited to the millimeter wave MEC in the calculation decision problem, that is, the decision calculation task is executed at the user side or the edge server side. Some millimeter wave MEC calculation unloading transmission strategies are only suitable for single user scenes, and have low unloading transmission energy efficiency and large transmission delay.
Disclosure of Invention
The invention aims to provide a millimeter wave MEC unloading and transmitting method based on a circular game algorithm, so that unloading and transmitting energy efficiency and transmission delay are optimized to the greatest extent in a multi-user scene.
In order to achieve the purpose, the technical scheme of the invention comprises the following steps:
1. a millimeter wave MEC unloading transmission method based on a circular game algorithm is characterized by comprising the following steps:
1) the non-cooperative game theory method is utilized to set user matching parameters during unloading transmission:
setting transmission power, transmission rate, matching probability and penalty coefficient;
setting a fixed user and a non-fixed user;
setting a pairing set NP, an unpaired set UP and a paired set AP of the current round;
defining a reward function as the product of the matching probability and the transmission rate;
defining a penalty function as a linear weighting of the transmission power and its inverse;
2) each fixed user and each non-fixed user form a matching pair, and the reward function R of each matching pair is calculatedi,j(S) and a penalty function Ci,j(S) and summing the two functions to obtain a utility function Ui,j(S), wherein i represents the ith fixed user, j represents the jth non-fixed user, and S represents a pairing set type;
3) according to the maximum utility function rule, fixed users m sequentially search optimal non-fixed users n for matching;
4) judging whether the current matching pair in 3) and the existing matching pair have the same non-fixed user n:
if so, perform 5);
if not, then execute 6);
5) comparing the utility function of the current matching pair in 3) with the utility function of the 'existing matching pair' of the non-fixed user n:
if in 3)Utility function U of front matching pairsm,n(S) is larger, the corresponding matching pair (m, n) is successfully matched, and the existing matching pair is broken;
otherwise, returning to 3), the fixed user m selects the non-fixed user n' with suboptimal performance;
6) checking whether all fixed users finish pairing:
if yes, completing user matching, and performing unloading transmission on each group of paired users based on the NOMA mechanism to complete MEC data unloading;
otherwise, return to 3).
The invention adopts the matching pair transmission process based on the circulating game, and has the following advantages:
firstly, compared with the existing traversal algorithm, the time complexity O (n ^2) approaching a quadratic polynomial can be reduced to the time complexity O (n) approaching a first-order polynomial.
Secondly, compared with the existing greedy algorithm, when the number of the matched pairs and the total transmission energy are respectively changed, the energy efficiency-time delay balance function can be better optimized.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
fig. 2 is a graph of energy efficiency versus delay function for simulated matching pairs when different numbers of pairs are considered using the present invention and a greedy algorithm of the prior art.
Fig. 3 is a graph of energy efficiency-delay tradeoff functions for a simulated matched pair when increasing total transmitted energy using the present invention and a prior greedy algorithm.
Detailed description of the invention
In order to make the object and technical solution of the present invention clearer and clearer, embodiments and effects of the present invention are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, the implementation steps of this example are as follows:
and step 1, setting user matching parameters during unloading transmission by using a non-cooperative game theory method.
The non-cooperative game theory method belongs to game theory, is an important branch of modern mathematics operational research, and means that when one party determines game strategies in a non-cooperative game process with limited game times, if the strategies selected by the other party in the game are the best strategies based on the game strategy combination, the strategy aggregate solutions selected by the two parties can reach a stable optimal solution.
The step sets user matching parameters during unloading transmission according to a non-cooperative game theory method, and the method is realized as follows:
1.1) setting transmission power W, transmission rate r, probability matching probability p for selecting the pairing set of the current round, matching probability q for selecting the unpaired set of the current round, and two penalty coefficients k1 and k2 with different values;
1.2) the game-setting parties have 2N, wherein the fixed users have N, the non-fixed users have N, the fixed users respectively represent fixed users i with fixed transmission time and non-fixed users j with unfixed transmission time, i, j is in the middle of {1,2, … N }, and the transmission time T of the non-fixed users isjLess than or equal to the transmission time T of the fixed useri；
1.3) setting a pairing set NP, an unpaired set UP and a paired set AP of the current round;
1.4) defining a reward function Ri,j(S) is the match probability betai,j(S) product with transmission rate r; defining a penalty function Ci,j(S) is a linear weighting of the transmission power W and its inverse;
1.5) defining a utility function Ui,jAnd (S) is the sum of the reward function and the penalty function.
And 2, calculating a user utility function.
2.1) forming a matching pair by each fixed user i and each non-fixed user j, wherein i, j belongs to {1,2, … N }, and N represents the total number of the matching pairs;
2.2) calculating the matching probability of each matching pair:
wherein, p represents the probability of selecting the pairing set NP of the current round, q represents the probability of selecting the unpaired set UP of the current round, i represents the ith fixed user, j represents the jth non-fixed user, and S represents the pairing set type;
2.3) calculating the reward function of the matching pair according to the matching probability, namely multiplying the matching probability of the matching pair by the transmission rate R to obtain the reward function R of the matching pairi,j(S)：
Ri,j(S)＝βi,j(S)·r；
2.4) calculating the penalty function C of each matching pairi,j(S), that is, the transmission power is linearly weighted with its inverse:
Ci,j(S)＝k1·W+k2/W，
wherein k is1,k2Are two penalty factors of different values, taken in this example but not limited to k1＝0.7，k20.3W is transmission power;
2.5) reward function Ri,j(S) and a penalty function Ci,j(S) summing to obtain utility function U of each matching pairi,j(S)：
And 3, judging whether the matching pair is successfully matched or not by using the utility function.
3.1) judging whether the matching pair of the fixed user is successfully matched according to the maximum utility function rule:
selecting a maximum value of a utility function of a matching pair formed by each fixed user i and all non-fixed users j, wherein the fixed users are successfully paired, and the rest are the fixed users which are not successfully paired;
3.2) fixed users i sequentially search the optimal non-fixed users j for matching:
3.2.1) forming matching pairs by using an unpaired successful fixed user i and all non-fixed users j, and calculating utility function values U of the matching pairsi,j(S)，j∈{1,2,…N}；
3.2.2) from Ui,1(S)～Ui,N(S) selecting the maximum value U of the utility functioni,n(S), then the maximum value U of the functioni,n(S) corresponding to "not matchingAnd the successful fixed user i and the non-fixed user n are matched pairs successfully.
And 4, repeating the matching judgment.
In all successfully matched matching pairs, judging whether the same non-fixed user n forms a matching pair with a plurality of fixed users:
if yes, repeated matching exists, and step 5 is executed;
if not, then no repeated matching exists, then executing step 6;
and 5, breaking repeated matching.
Comparing the sizes of the utility functions of the repeatedly matched matching pairs, reserving the matching pair with the largest utility function, and breaking the rest matching pairs;
returning the broken matching pairs to the step 3, and selecting the suboptimal non-fixed users by the rest fixed users.
And 6, detecting the final condition of the matching completion.
Comparing the successful match logarithm to the total match logarithm;
if the values of the two are equal, the user matching is judged to be completed, and all matching pairs are subjected to non-orthogonal multiple access (NOMA) transmission according to the matching completing sequence to complete the data unloading of the Mobile Edge Computing (MEC);
otherwise, returning to the step 3. This can be further illustrated by the following simulations:
1. simulation conditions are as follows:
the millimeter wave network comprises 9 macro base stations and 27 micro base stations, and the interval between every two macro base stations is 1 kilometer. And setting a communication frequency band as a W frequency band, setting the available total bandwidth as 1GHz, setting the maximum transmission power of each macro base station as 46dBm and the noise power as-174 dBm/Hz.
2. Simulation content:
simulation 1, namely performing MEC task unloading transmission simulation on the millimeter wave network by respectively using the circular game algorithm and the existing greedy algorithm, and calculating an energy efficiency-delay function value when the matching logarithm is changed, wherein the result is shown in figure 2. The abscissa is the number of matching groups, and the ordinate is the energy efficiency-delay tradeoff value required by 10000 times of simulation averaging.
As can be seen from the simulation result of fig. 2, under the same number of matching groups, the energy efficiency-delay tradeoff value of the millimeter wave network for performing MEC task offloading transmission by using the method of the present invention is lower than the energy efficiency-delay tradeoff function value by using the greedy algorithm, and the advantages of the method of the present invention are more obvious as the number of matching groups increases.
And 2, performing MEC task unloading transmission simulation on the millimeter wave network by respectively using the method and the conventional greedy algorithm, and calculating the simulation of the energy efficiency-time delay balance value when the total transmission energy is changed, wherein the result is shown in FIG. 3. Wherein, the abscissa is total energy, and the ordinate is energy efficiency-time delay balance value required averagely in simulation 10000 times. As can be seen from the simulation result in fig. 3, under the condition of a certain total energy, the energy efficiency-delay tradeoff value of the millimeter wave network for performing MEC task offloading transmission by using the method of the present invention is lower than the energy efficiency-delay tradeoff value by using the greedy algorithm, and as the total energy increases, the energy efficiency-delay of the method of the present invention decreases more than the greedy algorithm.
Claims (5)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110102950.5A CN112954739B (en) | 2021-01-26 | 2021-01-26 | Millimeter wave MEC offload transmission method based on circular game algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110102950.5A CN112954739B (en) | 2021-01-26 | 2021-01-26 | Millimeter wave MEC offload transmission method based on circular game algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112954739A true CN112954739A (en) | 2021-06-11 |
| CN112954739B CN112954739B (en) | 2023-02-07 |
Family
ID=76236933
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110102950.5A Active CN112954739B (en) | 2021-01-26 | 2021-01-26 | Millimeter wave MEC offload transmission method based on circular game algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112954739B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110191129A1 (en) * | 2010-02-04 | 2011-08-04 | Netzer Moriya | Random Number Generator Generating Random Numbers According to an Arbitrary Probability Density Function |
| EP3457664A1 (en) * | 2017-09-14 | 2019-03-20 | Deutsche Telekom AG | Method and system for finding a next edge cloud for a mobile user |
| CN109672568A (en) * | 2019-01-11 | 2019-04-23 | 南京邮电大学 | A kind of method of the edge calculations network Green energy distribution and Coordination Pricing |
| CN110113190A (en) * | 2019-04-24 | 2019-08-09 | 西北工业大学 | Time delay optimization method is unloaded in a kind of mobile edge calculations scene |
| CN110233686A (en) * | 2018-06-20 | 2019-09-13 | 桂林电子科技大学 | Stabilization matching method of the cognition wireless network based on Game with Coalitions |
| CN110708713A (en) * | 2019-10-29 | 2020-01-17 | 安徽大学 | A mobile edge computing mobile terminal energy efficiency optimization method using multi-dimensional game |
| US20200275313A1 (en) * | 2019-02-26 | 2020-08-27 | Verizon Patent And Licensing Inc. | Method and system for scheduling multi-access edge computing resources |
-
2021
- 2021-01-26 CN CN202110102950.5A patent/CN112954739B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110191129A1 (en) * | 2010-02-04 | 2011-08-04 | Netzer Moriya | Random Number Generator Generating Random Numbers According to an Arbitrary Probability Density Function |
| EP3457664A1 (en) * | 2017-09-14 | 2019-03-20 | Deutsche Telekom AG | Method and system for finding a next edge cloud for a mobile user |
| CN110233686A (en) * | 2018-06-20 | 2019-09-13 | 桂林电子科技大学 | Stabilization matching method of the cognition wireless network based on Game with Coalitions |
| CN109672568A (en) * | 2019-01-11 | 2019-04-23 | 南京邮电大学 | A kind of method of the edge calculations network Green energy distribution and Coordination Pricing |
| US20200275313A1 (en) * | 2019-02-26 | 2020-08-27 | Verizon Patent And Licensing Inc. | Method and system for scheduling multi-access edge computing resources |
| CN110113190A (en) * | 2019-04-24 | 2019-08-09 | 西北工业大学 | Time delay optimization method is unloaded in a kind of mobile edge calculations scene |
| CN110708713A (en) * | 2019-10-29 | 2020-01-17 | 安徽大学 | A mobile edge computing mobile terminal energy efficiency optimization method using multi-dimensional game |
Non-Patent Citations (7)
| Title |
|---|
| TIAN ZHANG等: "Data Offloading in Mobile Edge Computing: A Coalition and Pricing Based Approach", 《IEEE ACCESS》 * |
| YALI CHEN等: "Energy Efficient Resource Allocation and Computation Offloading in Millimeter-Wave based Fog Radio Access Networks", 《ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC)》 * |
| ZHONGLING ZHAO等: "Matching theory assisted resource allocation in millimeter wave ultra dense small cell networks", 《ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC)》 * |
| 余翔等: "移动边缘计算中卸载策略与功率的联合优化", 《计算机工程》 * |
| 周持盈等: "一种基于移动边缘计算与波束赋形的任务分配算法", 《电子制作》 * |
| 廖晓闽等: "基于深度强化学习的蜂窝网资源分配算法", 《通信学报》 * |
| 张海波: "车联网中基于NOMA-MEC的卸载策略研究", 《电子与信息学报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112954739B (en) | 2023-02-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhao et al. | System optimization of federated learning networks with a constrained latency | |
| CN110418416A (en) | Resource allocation method based on multi-agent reinforcement learning in mobile edge computing system | |
| CN111447619A (en) | A method for joint task offloading and resource allocation in mobile edge computing networks | |
| CN109089272B (en) | Brent-style latency optimization method for mobile edge computing in multi-base station scenarios | |
| Chen et al. | Delay guaranteed energy-efficient computation offloading for industrial IoT in fog computing | |
| CN111565380B (en) | Hybrid offloading method based on NOMA-MEC in the Internet of Vehicles | |
| CN113590279B (en) | Task scheduling and resource allocation method for multi-core edge computing server | |
| CN108924938A (en) | A kind of resource allocation methods for wireless charging edge calculations network query function efficiency | |
| CN112512070B (en) | Multi-base-station cooperative wireless network resource allocation method based on graph attention mechanism reinforcement learning | |
| CN110535936A (en) | A kind of energy efficient mist computation migration method based on deep learning | |
| CN113556764A (en) | Method and system for determining calculation rate based on mobile edge calculation network | |
| CN113507712A (en) | A Resource Allocation and Computing Task Offloading Method Based on Alternating Direction Multipliers | |
| Wang et al. | Multi-agent reinforcement learning-based user pairing in multi-carrier NOMA systems | |
| CN116366576A (en) | Method, device, equipment and medium for scheduling computing power network resources | |
| CN115955479A (en) | Task rapid scheduling and resource management method in cloud edge cooperation system | |
| Zhang et al. | Correlation-based device energy-efficient dynamic multi-task offloading for mobile edge computing | |
| Fan et al. | Task caching and computation offloading for muti-user mobile edge computing network | |
| CN111212108A (en) | Multi-user parallel migration method based on non-orthogonal multiple access and mobile edge computing | |
| CN110035539A (en) | One kind being based on the matched resource optimal distribution method of correlated equilibrium regret value and device | |
| CN111682915B (en) | A kind of spectrum resource self-allocation method | |
| CN105916197B (en) | The power adaptive method that social credibility drives in D2D network | |
| CN112954739B (en) | Millimeter wave MEC offload transmission method based on circular game algorithm | |
| Li et al. | Suitability-based edge server placement strategy in 5G ultra-dense networks | |
| CN115633369A (en) | A Multi-Edge Device Selection Method Oriented to User Task and Power Joint Allocation | |
| CN110933687A (en) | A method and system for user uplink and downlink access based on decoupling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |